El año de 1998 se constituyó el Acuerdo de Competitividad de la Cadena Láctea Colombiana, el cual se precedió de un trabajo con el apoyo y decidida participación de los Ministerios de Agricultura y Desarrollo Rural, Comercio Exterior, Desarrollo Económico y Salud, así como también el Instituto Interamericano de Cooperación para la Agricultura, IICA, Acoleche, Analac, Asociación de Procesadores Independientes de Leche; Cámara de Industria de Alimentos de la ANDI, Fedecoleche y Fedegan. Este fue el primer paso para concertar entre el sector productivo y el Gobierno Nacional. Este Acuerdo está constituido por estrategias, proyectos y acciones que comprometen al Gobierno, ganaderos, industriales Y cooperativas con una política de concertación, consenso y competitividad. Uno de los resultados del Acuerdo fue la Creación del Consejo Nacional Lácteo Colombiano, como organismo administrador de éste y promotor del desarrollo del sector, que además de fijar los precios de la leche, dio confianza para la inversión privada y permitió el arranque al desarrollo industrial de la leche en Colombia.

We explore how we can use synthetically generated RGB-D training data from a near photo-realistic game engine to train modality-specific person detectors. We perform ablation studies on a challenging, real-world dataset which we recorded using a Kinect v2 RGB-D sensor in multiple warehouse environments. Through extensive use of domain randomization techniques, we synthesize a realistic and highly varied training set of challenging intralogistics scenarios as observed from a mobile robot, comprising persons in confined and cluttered indoor spaces. We then train the detector layers of a YOLOv3 model from scratch on our synthetic RGB and jet-encoded depth images. While for the RGB case, we still observe a domain gap of 6 points in mAP compared to a pretrained COCO model, results indicate that by exploiting simulation, an immense manual labeling effort needed to prepare large-scale datasets such as MS COCO might be unnecessary for the depth modality. We further find that filtering of highly occluded groundtruth boundin boxes during training, as well as modeling of time-of-flight sensor noise characteristics has a positive impact on model performance. We also provide an initial set of qualitative results on our real-world dataset.

An accurate understanding of the environment is key for a robot in order to execute tasks safe and efficiently. In the field of perception, after the introduction of deep learning, computer vision tasks have made big leaps, surpassing even the human inference capability. As a trade-off, big amounts of annotated data were required to train such algorithms. On its own, the collection of annotated dataset is a highly time consuming activity prone to human errors, setting a limit to the maximum achievable performance. In this sense, annotations (quality) and samples (quantity) bound the optimization of perception algorithms. One extra challenge encountered when training object detectors for robotics applications is that the sensor setup can be multi-modal, and vary significantly between robots. This work explores how to generate and use synthetic RGB-D training data from a near photo-realistic game engine to train modality-specific person detectors, and perform ablation studies on a challenging, real-world dataset recorded using a reference RGB-D sensor in different intralogistics environments. A virtual RGB-D camera was implemented, leveraging the underlying deferred rendering architecture. Multiple environments were tailored, exploring various data augmentation techniques and enabling the comparison between different types of synthetic data. Detection layers of a pre-trained object detector network have been trained from scratch for the RGB and depth modality, with the latter being transformed by applying a Jet-colormap. Compared to a pre-trained network, a domain gap of 5 mPA points was still present for RGB images. Meanwhile with synthetic (15k) and real (1.5k) depth images, it was already possible to train robust human detectors. Comparing simulation features against data preparation, filtering annotations had a major impact on performance than adopting an explicit time-of-flight sensor model.

Within the last decade circuit quantum electrodynamics (circuit QED) [1] has proven to be an excellent platform to study the coherent interaction between an artificial atom and a light field. Here an artificial atom, or more precisely an effective two level system, is realized using superconducting circuitry while the cavity is realized as a microwave resonator. Recently, an effective two level system realized using a semiconductor double quantum dot (DQD) has been coupled to a microwave resonator. In this hybrid system, interactions between the dipole moment of (single) electrons in a DQD and the electromagnetic field in a microwave resonator have been observed (2,3). Using the same architecture, we initially discuss experiments performed with a resonator sample in order to investigate design parameters influencing its quality factor. With the aim of enabling high bandwidth correlation measurements, we then present the design and implementation of parallel digital filters in a digital signal processing platform. We finally report on experiments to use the microwave resonator as a tool to explore fundamental processes occurring in a DQD driven out-of-equilibrium. In particular, we perform exploratory attempts to probe the microwave radiation emitted from a voltage-biased DQD by means of first and second order correlation function measurements. Resumen: En la última década, la electrodinámica cuántica de circuitos (circuit QED) [1] ha demostrado ser una excelente plataforma para estudiar la interacción coherente entre un átomo artificial y un campo de luz. El esquema consiste en un átomo artificial, o más precisamente un sistema efectivo de dos niveles, implementado usando circuitos superconductors, mientras que la cavidad se implementa como un resonador de microondas. Recientemente, se ha acoplado un sistema de dos niveles, implementado utilizando un punto cuántico doble de semiconductores (DQD), a un resonador de microondas. En este sistema híbrido, se han observado interacciones entre el momento dipolar de electrones (individuales) en un DQD y el campo electromagnético en un resonador de microondas [2,3]. Utilizando la misma arquitectura, inicialmente discutimos experimentos realizados con un resonador para investigar los parámetros de diseño que influyen en su factor de calidad. Con el objetivo de permitir mediciones de correlación de alto ancho de banda, presentamos el diseño e implementación de filtros digitales paralelos en una plataforma de procesamiento digital de señales. Finalmente, presentamos experimentos para utilizar el resonador de microondas como herramienta para explorar los procesos fundamentales que ocurren en un DQD operado fuera de equilibrio. En particular, realizamos intentos exploratorios para sondear la radiación de microondas emitida por un DQD (polarizado con una diferencia de voltage) por medio de mediciones de la función de correlación de primer y segundo orden.

In 2019, the European Commission published the “2030 Clean Energy for All Europeans” package, as part of the 2050 Energy roadmap. To comply with the European directives, Italy published the National Energy and Climate Plan setting a target of 55% for RES share in electricity generation by 2030. This high RES generation target raises concerns about the operational flexibility of the Italian power system. The flexibility of the Italian power system was measured using currently available flexibility frameworks and indexes (e.g.: AFPER, FAST2). Hence, the 2030 Base Scenario and 15 alternative scenarios were simulated using Promed Grid, a market tool developed by CESI and able to perform a coordinated hydro- thermal dispatching over a one-year horizon, considering the adoption of some flexibility sources such as storage systems and demand response. The results were then compared to characterize the performance of each flexibility source and its influence on the power system. BESS, PHS, and DR showed a level of complementarity while covering flexibility needs and controlling price spikes. Daily needs are covered mainly by BESS and weekly needs by PHS, mainly during the summer since both technologies were more used due to high solar power generation. Conversely, peak shedding DR was primarily activated in winter since prices are higher and storage systems lees used. In the case of V2G and DR, the role of aggregators is vital to have a representative capacity to be offered in the power markets. Finally, from a holistic perspective, the benefits of implanting all flexibility source are higher than the costs. Nevertheless, such benefits should be redistributed to incentivize the deployment of certain sources with low benefit-cost ratios. The results of this thesis can serve as a starting point to delve into the technical performance and economic implications of each flexibility source. In addition, it highlighted the advantages for Italy of boosting and supporting some schemes such as DR and V2G, as well as incentivizing the installation of storage systems.

Abstract: Complex networks emerge as a natural framework to describe real-life phenomena involving a group of entities and their interactions, i.e., a social net-work. Furthermore, other problems involve a collection of networks, such as multi-layer, temporal, or brain networks (connectomes). With the increasing availability of graph-shaped data and associated meta-data, i.e., node or edge attributes, machine learning (ML) techniques on networks have risen in popularity. However, there are still several challenging issues to be addressed concerning feature engineering, graph representation, visualization, and general graph mining applications. This thesis contributes to bridging the gap between ML and complex net-works. We investigate dynamics on networks such as discrete and continuous- time random walks, in two directions. First, we develop a multi-scale anomaly detection algorithm on attributed networks. We exploit the link between graph signal processing and the Markov stability framework used in community detection, for spotting anomalous nodes in multiple contexts of the network. Second, we introduce a generalization of assortativity on networks operating on scalar and categorical node attributes. These coefficients turn out to be useful descriptors to build network fingerprints, so that we can perform supervised graph classification, i.e., predicting the toxicity of molecules or classifying Reddit discussion threads. Besides, we develop an unsupervised approach to learn graph embeddings from a collection of networks. By learning a non-linear mapping from input graphs to a lower-dimensional feature space, we obtain useful graphs representations used in graph visualization, clustering, and classification, e.g., predicting people’s gender based on their structural connectome. Finally, we propose a principled approach to identify stable biomarkers for schizophrenia diagnosis in the human connectome. From an ML perspective, the problem is stated as an embedded feature selection scheme for graph classification. By doing so, we identify the affected-core in schizophrenia, highlighting increasing performance when structural and functional neural features are combined. Resumen: Las redes complejas emergen como marco general en la descripción de problemas reales que incluyen entidades y sus interacciones, ex., redes sociales. Además, otros problemas incluyen colecciones de redes tales como redes multi nivel, redes temporales o redes del cerebro. Con el incremento de la disponibilidad de datos en forma de grafos y metadatos asociados, ex., atributos en nodos o ejes, técnicas de aprendizaje automático en redes han incrementado en popularidad. Sin embargo, múltiples desafíos siguen aún sin ser resueltos, relacionados con representación de grafos, ingeniería de características en grafos, visualización y aplicaciones generales de minería de datos sobre grafos. Esta tesis doctoral contribuye a cerrar la brecha entre dos campos del conocimiento emergentes: aprendizaje automático y ciencia de redes. En esta tesis se investigan sistemas dinámicos sobre redes en la extracción de características estructurales en redes y se introduce una generalización de asortatividad en redes. Dicha asortatividad permite definir descriptores de grafos útiles en la construcción de huellas digitales de redes utilizadas en problemas de aprendizaje supervisado en clasificación de grafos, ex., predicción de toxicidad en moléculas o clasificación de redes sociales Reddit. Además, esta tesis propone un nuevo método no supervisado para crear embeedings de grafos. Gracias a la estimación de una función no lineal, se puede mapear grafos enteros en espacios euclideanos, preservando la similaridad entre grafos de origen. Finalmente, se propone un algoritmo para el diagnóstico de esquizofrenia en el cerebro humano usando connectomes, destacando las regiones del cerebro afectadas por la enfermedad mostrando resultados de alta confiabilidad utilizando distintas representaciones neuronales.

Abstract: Infrastructure plays an essential role in the economy and has a positive impact on social stability. In this regard, bridges are a fundamental component of infrastructure; however, most of the bridges currently under operation were constructed a long time ago and have even passed their useful life. Certainly, Flaig & Lark (2000) noted that most of the current bridges in the UK were built between 1,950 and 1,970. Furthermore, Steve Kroft (2014) indicated that almost 70,000 bridges (one out of every nine bridges) in America are structurally deficient. Consequently, it is necessary to develop fast and efficient inspection techniques that meet current requirements, and that could potentially replace the highly- subjective visual observation methods. This research presents a critical comparison among different Machine-Learning algorithms that are currently used in image classification problems and applied them into the automated recognition of cracks in reinforced concrete images so that they can be used as an alternative for the existing inspection methods. Moreover, ensembles with different combinations of the evaluated models were tested as an incentive to improve the performance of individual models. In total, four algorithms that based their operation in mathematical formulations and 10 different architectures of Convolutional Neural Networks were included. The results showed that the Random Forest classifier outperformed the other mathematical classifiers and performed similarly to the best of the neural networks. In addition, it was found that the ensembled models achieved little or no improvement concerning the best of the classifiers included in the stack. Resumen: La infraestructura desempeña un papel esencial en la economía de los países y tiene un efecto positivo en la estabilidad social de las personas. A este respecto, los puentes son un componente fundamental de la infraestructura; sin embargo, la mayoría de los puentes actualmente en funcionamiento se construyeron hace mucho tiempo e incluso han pasado su vida útil. Ciertamente, Flaig y Lark (2000) observaron que la mayoría de los puentes actuales del Reino Unido se construyeron entre 1.950 y 1.970. Además, Steve Kroft (2014) indicó que casi 70.000 puentes (uno de cada nueve puentes) en USA son estructuralmente deficientes. Por consiguiente, es necesario desarrollar técnicas de inspección rápidas y eficientes que cumplan los requisitos actuales y que puedan sustituir a los métodos de inspección visual altamente subjetivos. Esta investigación presenta una comparación crítica entre los diferentes algoritmos de aprendizaje a máquina que se utilizan actualmente en los problemas de clasificación de imágenes y los aplica al reconocimiento automatizado de grietas en las imágenes de concreto reforzado para que puedan utilizarse como alternativa a los métodos de inspección existentes. Además, se ensayaron conjuntos con diferentes combinaciones de los algoritmos de aprendizaje evaluados como incentivo para mejorar el rendimiento de los modelos individuales. En total, se incluyeron cuatro algoritmos que basaban su funcionamiento en formulaciones matemáticas y 10 arquitecturas diferentes de Redes Neuronales Convolucionales. Los resultados mostraron que el clasificador Random Forest superó a los otros clasificadores matemáticos y tuvo un rendimiento similar a la mejor de las redes neuronales. Además, se encontró que los modelos ensamblados lograron poca o ninguna mejora en lo que respecta al mejor de los clasificadores incluidos en el conjunto.

Resumo Neste trabalho, é apresentado um modelo de programação estocástica (MPE) para resolver o problema de planejamento da expansão da rede de transmissão multiestágio (PERTM). O MPE proposto considera fontes de energia renovável intermitente (FERIs) e a alocação de sistemas de armazenamento de energia (SAEs). A natureza estocástica da demanda e das FERIs é modelada através de períodos representativos (PRs). Os PRs são períodos caraterís-ticos construídos a partir de séries temporais que permitem representar o comportamento do sistema elétrico de potência (SEP). Variações de curto prazo da demanda e das FERIs, a cor-relação horária entre essas variáveis aleatórias, e a sua caracterização de acordo com a regi-ão do SEP a que pertencem são incluídas nos PRs, fornecendo um aceitável esforço compu-tacional para resolver o problema de PERTM. Com base nos PRs, o MPE proposto é formu-lado como um modelo equivalente determinístico de programação linear inteira mista (PLIM) para um conjunto de cenários. O modelo de PLIM incorpora perdas de potência ativa e contingências N-1. Investimento em construção de linhas de transmissão (LTs) e novos SAEs são otimizados. Um método eficiente para reduzir o espaço de busca combinatório e lidar com a dimensionalidade do problema de PERTM foi desenvolvido. Os sistemas de tes-te: Garver de 6 barras e IEEE de 24 barras; e o sistema de transmissão norte-colombiano foram usados para validar o modelo proposto. Os resultados mostram que a otimização si-multânea de LTs e SAEs fornece melhores e mais baratos planos de expansão do que as abordagens tradicionais. A eficácia dos SAEs para deslocar blocos de energia elétrica ao longo dos PRs, adiar investimentos e aliviar o congestionamento da rede é evidenciada. Resumen: En este trabajo se presenta un modelo de programación estocástica (MPE) para resolver el problema de planeamiento multietapa de la expansión de la red de transmisión (PERTM). El MPE propuesto considera las fuentes de energía renovables intermitentes (FERIs) y la instalación de sistemas de almacenamiento de energía (SAEs). La naturaleza estocástica de la demanda y las FERIs se modela a través de períodos representativos (PRs). Los PRs son periodos característicos construidos a partir de series de tiempo que permiten representar el comportamiento del sistema eléctrico de potencia (SEP). Las variaciones de corto plazo en la demanda y las FERIs, la correlación horaria entre estas variables aleatorias y su caracterización según la región del SEP a la que pertenecen se incluyen en los PRs, proporcionando un esfuerzo computacional aceptable para resolver el problema PERTM. Sobre la base de los PRs, el MPE propuesto se formula como un modelo equivalente determinístico de programación lineal entera mixta (PLIM) para un conjunto de escenarios. El modelo PLIM incorpora pérdidas de potencia activa y contingencias N-1. Se optimizan las inversiones en la construcción de líneas de transmisión (LT) y nuevos SAEs. Se desarrolló un método eficiente para reducir el espacio de búsqueda combinatoria y mejorar la eficiencia del modelo. Los sistemas de prueba: Garver de 6 barras y IEEE de 24 barras; y el sistema de transmisión del norte de Colombia fueros usados para validar el modelo propuesto. Los resultados muestran que la optimización simultánea de LTs y SAEs proporciona planes de expansión mejores y más económicos que los enfoques tradicionales. Se evidencia la efectividad de los SAEs en desplazar bloques de energía en el tiempo, posponer inversiones y aliviar la congestión de la red.

Abstract: Electric vehicles (EVs) have gained great popularity in recent years in the United States. The reduction in the cost of EVs and the environmental regulations that seek to reduce the polluting gases generated by conventional petrol/diesel cars drive up the demand for electric vehicles. The traditional idea is to consider EVs as energy consumers, however Vehicle-to-Grid (V2G) is a technology that allows the batteries of parked EVs to operate as energy sources. V2G technology allows to store energy in EVs and then release it when this energy is needed. The implementation of V2G technologies can provide benefits to utility companies but also presents a series of challenges that must be analyzed and evaluated. V2G technology has a great potential to help Austin Energy use the energy stored in the EVs. This research focuses on the status of charging systems for EVs in Austin, the basic aspects of V2G technology, the standards related to V2G, and aims to provide specific and valuable applications of V2G technology for Austin Energy.

Abstract: The dispersion of pollutants from urban sources needs to be investigated and the mechanisms which have an influence on the dispersion need to be totally understood. UAV-based systems offer great potential for mobile exploration of air pollutants in the lower atmosphere due to the high maneuverability of multi-rotor UAVs. The aim of this study was to develop a UAV platform capable of performing high-resolution three-dimensional profiling of pollutants such as particulate matter (PM), ultrafine particles (UFP), black carbon (BC), as well as meteorological parameters including temperature, relative humidity, pressure, wind speed, and wind direction. This study included a smoke test to examine the propeller-caused airflows. The results showed that the sensor air inlets and wind sensor should be located at least 90 cm above the propeller level. In addition, a field test was conducted to evaluate the stability of the UAV platform and the in-flight performance of the sensors. During the three flights carried out, the UAV platform was stable, and the data collected with the sensors were consistent. Finally, a three-day measurement campaign was conducted to examine the short-term three-dimensional variation of the traffic-related pollutant concentrations near the federal road B14. The vertical profiles showed that concentrations were higher on the ground due to the proximity to the source and that high wind speeds help the fast dispersion of the pollutants. The horizontal profiles showed that the pollutant concentrations were higher at the roadside and decreased with increasing distance from the road. In conclusion, this UAV platform represents a low-cost, practical, and reliable method to study the three-dimensional distribution of pollutants near the source. Resumen: La dispersión de contaminates originado por fuentes urbanas necesita ser investigado y los mecanismos que tienen influencia en la dispersión deben ser totalmente comprendidos. Los sistemas basados en VANT ofrecen gran potencial en la exploración de contaminantes del aire en la parte inferior de la atmósfera debido a la alta manejabilidad de los VANT multirotor. El objetivo de este estudio fue el desarrollo de una plataforma, usando un VANT, capaz de realizar perfiles tridimensionales de alta resolución de contaminantes como material particulado, particulas ultrafinas, carbón negro, así como parámetros meteorológicos incluyendo temperatura, humedad relativa, presión, velocidad y dirección del viento. Este estudio incluyó una prueba de humo para examinar los flujos de aire provocados por las hélices del VANT. Los resultados mostraron que las entradas de aire de los sensores y el sensor de viento deben ubicarse al menos 90 cm por encima del nivel de la hélice. Además, se realizó una prueba de campo para evaluar la estabilidad del VANT equipado y el desempeño de los sensores en vuelo. Durante los tres vuelos realizados, VANT se mantuvo estable y los datos recolectados con los sensores fueron consistentes. Finalmente, se llevaron a cabo mediciones durante tres días para examinar la variación tridimensional a corto plazo de las concentraciones de contaminantes relacionados con el tráfico cerca de la carretera federal B14 en Stuttgart. Los perfiles verticales mostraron que las concentraciones eran mayores en el suelo debido a la proximidad a la fuente y que las altas velocidades del viento ayudan a la rápida dispersión de los contaminantes. Los perfiles horizontales mostraron que las concentraciones de contaminantes eran más altas al costado de la carretera y disminuían al aumentar la distancia desde la carretera. En conclusión, este VANT equipado representa un método de bajo costo, práctico y confiable para estudiar la distribución tridimensional de contaminantes cerca de la fuente.